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Multiobjective Optimization of the Breathing System of an Aircraft Two Stroke Supercharged Diesel Engine

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Multiobjective Optimization of the Breathing System of an Aircraft Two Stroke Supercharged Diesel Engine Available online at www.sciencedirect.com ScienceDirect Energy Procedia 82 ( 2015 ) 31 – 37 ATI 2015 - 70th Conference of the ATI Engineering Association Multiobjective optimization of the breathing system of an aircraft two stroke supercharged Diesel engine Antonio Paolo Carluccia,*, Antonio Ficarellaa, Domenico Laforgiaa, Gianluca a Trullo aDepartment of Engineering for Innovation, University of Salento, Via per Monteroni, Lecce 73100, Italy Abstract One of the factors limiting the utilization of piston internal combustion engines for aircraft propulsion is the performance decrease increasing the altitude of operation. This is due to the negative effect of air density reduction increasing the altitude on cylinder filling. A solution to this problem is represented by the engine supercharging. Unfortunately, in two stroke engines, the cylinder filling efficiency is antithetical to the cylinder scavenging efficiency. With the aim of guaranteeing an optimal balance between engine performance and specific consumption, an engine breathing system optimization is needed. In this work, the results obtained running a multi-objective optimization procedure aiming at performance increase and fuel consumption reduction of an aircraft two stroke supercharged diesel engine at various altitudes are analyzed. During the optimization procedure, several geometric parameters of the intake and exhaust systems as well as geometric and operating engine parameters have been varied. Then, a multi-objective optimization algorithm based on genetic algorithms has been run to obtain the configurations optimizing the engine performance at Sea Level (take-off conditions) and fuel consumption at 10680 m (cruise conditions). © 20152015 The The Authors. Authors. Published Published by Elsevier by Elsevier Ltd. This Ltd. is an open access article under the CC BY-NC-ND license (Selectionhttp://creativecommons.org/licenses/by-nc-nd/4.0/ and/or peer-review under responsibility). of ATI Peer-review under responsibility of the Scientific Committee of ATI 2015 Keywords: 2-strokes Diesel engine; scavenging, multi-objective optimization 1. Introduction In two stroke engines, the cylinder filling and emptying phases are driven by the difference between inlet and outlet pressure. Therefore, the supercharging system of a two stroke engine determines both the air density increase and the cylinder scavenging. Due to the strict interaction among supercharging, * Corresponding author. Tel.: -39 0832 297751; fax: +39 0832 297777. E-mail address: [email protected]. 1876-6102 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Scientific Committee of ATI 2015 doi: 10.1016/j.egypro.2015.11.879 32 Antonio Paolo Carlucci et al. / Energy Procedia 82 ( 2015 ) 31 – 37 scavenging and combustion, two stroke engines are very sensitive to the variation of engine working and geometric parameters as well as ambient conditions with altitude 0, 0. In order to obtain the best cylinders breathing mechanism, different supercharging architectures for a two stroke engine prototype (a single mechanical compressor supercharger; a turbocharger with a crankcase scavenging pump; a mechanical compressor combined with a turbocharger) have been numerically modeled and their performance compare dat sea level in 0. Performance are optimized using a turbocharger upstream a second compression stage, due to both compression power reduction and trapping efficiency increase. Multi-objective optimization processes based on genetic algorithms are often used to improve engine performance and to study the effect of input parameters variation on the engine behavior 0. In this work, the results obtained running a multi-objective optimization procedure aiming at reducing fuel consumption and increasing performance of an aircraft two stroke supercharged diesel engine at difference altitudes are analyzed. 2. Engine Model and Optimization Procedure The engine analyzed in the present work is a two stroke Diesel Engine – composed by six cylinders arranged in boxer configuration in two independent banks – for aircraft propulsion. Each bank is fed by a supercharging system composed by two turbochargers, an after-cooler and a common intake plenum. The scavenging system is “Uniflow”, with 14 inlet ports and 2 exhaust valves per cylinder. The engine main specifications are reported in Table 1, while its scheme is reported in Errore. L'origine riferimento non è stata trovata..The 0D-1D model of the engine has been realized in AVL BOOST software v2011.2. More details about the software are reported in 0 while details about the engine model and calibration can be found in 0. Table 1. Engine main specifications Cycle Two-Stroke Diesel Uniflow Bore/Stroke ratio 1 Compressor Ratio 17.2:1 Injection System Common Rail Design Engine Speed 2000 rpm Fig. 1. Engine model (single bank) Antonio Paolo Carlucci et al. / Energy Procedia 82 ( 2015 ) 31 – 37 33 With the aim of increasing the engine output power and reducing fuel consumption redesigning the intake and exhaust systems, the engine model was run varying several parameters on a range of allowed values. In Table 2 the engine parameters varied during simulations are listed. These parameters have been varied running the model at two different altitudes: Sea Level (SL) and 10670 m. Concerning the exhaust valves, once fixed the values for Exhaust Valve Opening (EVO), Closing (EVC) and maximum lift (HEV), the lift variation with Degrees Crank Angle (DCA) has been calculated through a routine implemented in Matlab and then transferred to the engine model in BOOST. Due to the considerably high number of combinations of parameters, an approach based on genetic algorithms was chosen in order to find the combinations leading to a better engine performance. Therefore, the engine thermo-dynamic model was interfaced with ESTECO ModeFrontier software 0. During the optimization procedure, several constraints were also introduced in order to discard those solutions not technically feasible: maximum in-cylinder pressure not higher than150 bar; maximum temperature at the after cooler outlet not higher than 473.15 K; maximum exchanged heat power in the after cooler not higher than 40 kW. Table 2. Engine input parameters Actual Lower Upper Central Input Parameter Unit Acronym Step Value Bound Bound Value Volumetric Compression Ratio [-] CR 17.2 13 18 15.5 0.2 Exhaust Valve Opening (after TDC) [DCA] EVO 80 60 110 85 5 Exhaust Valve Closing (after TDC) [DCA] EVC 250 220 270 245 5 Exhaust Valve Maximum Lift [mm] HEV 12 8 14 11 0,5 Intake Port Opening (after TDC) [DCA] IPO 115 100 130 115 2.5 Port Section [mm2] A 265 200 300 250 5 Number of Inlet Ports [-] n 14 10 19 14.5 1 Distance of Inlet Port from BDC [mm] HBI 2.5 1 3 2 0.25 Air Fuel Ratio [-] AFR 20 17 27 22 0.5 Pressure Ratio – High Pressure [-] PRHP 1.7 1.2 2.2 1.7 0.05 Compressor @ SL Pressure Ratio – Low Pressure [-] PRLP 1.75 1.2 2.2 1.7 0.05 Compressor @ SL Pressure Ratio – High Pressure [-] PRHP 2.3 2.2 3.4 2.8 0.05 Compressor @ 10670 m Pressure Ratio – Low Pressure [-] PRLP 3 2.2 3.4 2.8 0.05 Compressor @ 10670 m As previously said, the output variables to be optimized were the engine output power and specific fuel consumption. The Design of Experiment generated 30 random starting solutions; for each one of them, the genetic algorithm generated 70 combinations. The resulting 2100 configurations defined the Pareto Front, where every solution represents a non-dominated relative optimum 0.Once the Pareto front had been created, the best solutions have been defined as described in the following. 3. Data Analysis The Pareto front represents 0 a series of optimum points defined as “non-dominated solutions”. This means that each point composing the Pareto front is the best achievable solution using a specific set of 34 Antonio Paolo Carlucci et al. / Energy Procedia 82 ( 2015 ) 31 – 37 input parameters. However, given the significant amount of input parameters and levels set for the optimization procedure, a statistical approach was chosen to interpret the obtained results. In order to manage the high number of input data and their non-normal distribution trend, the t-Student distribution was chosen, and two meaningful statistic parameters have been calculated: the Effects Size (ES) and the Significance (S). As suggested by Ferguson 0, ES is an estimate of the magnitude of the effects of the association between two or more variables. The computation of this parameter is reported by Cohen 00;in this work it can be assumed, for sake of simplicity, that the magnitude of the effects that an input parameter has on an output one is directly proportional to the ES module. More specifically, when ES is positive, a direct relation between the input and the output occurs, whereas when ES is negative the relation is inverse. The latter statistic parameter is S. The significance level of a statistical hypothesis test is a fixed probability (referred to a fixed threshold value set as 0.05) of rejecting the null hypothesis 0 which represents a statistic value that has to be accepted or rejected according to the computed S value. To prevent from false claims the significance value has to be as low as possible and eventually equal to 0. 4. Discussion of Results As previously said, ES and S can help in recognizing which inputs have the largest effects on engine power (Pb) and specific fuel consumption
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